This assignment is for ETC5521 Assignment 2 by Team Cassowary comprising of Sahinya Akila and Xinrui Wang.

1 Introduction and Motivation - Rachel to edit

Gender and race have always been widely discussed topics in various social domains, including employment and earning. Fekedulegn et al. (2019) suggested that workplace discrimination and mistreatment varied significantly by race and gender in the US, this statement raises the interest on exploring and conducting a detailed analysis in regards to the employment and earnings across different industries in the USA, to find out if this statement is true and how significant gender and race are in affecting employment and earning.

The data used in this report is collected from tidytuesday, by looking through the employment status and earning from 2010 to 2020 across different races, genders as well as age groups in various industries in the US, the findings will assist with promoting fairness, equality and diversity in the workplace.

Analysis conducted and conclusions drew in this report are solely based on the datasets described under Data Description section, all records in the datasets are assumed to be accurate. Furthermore, due to the inadequate information in regions and inconsistency of time frame in the two datasets used in this report, the findings could be subject to potential bias.

2 Data Description - Sahinya to edit

The datasets originally come from BLS, specifically table cpsaat17 across several years.

The employed dataset tells about employed persons by industry, sex, race, and occupation through 2015 to 2020.

Variable Data Type Description
industry character Industry Group
major_occupation character Major occupation category
minor_occupation character Minor occupation category
race_gender character Race & Gender wise information
industry_total double Industry total count
employ_n double Number of people employed
year double Year

The earn dataset tells about weekly median earnings and number of persons employed by race/gender/age group through 2010 to 2020.

Variable Data Type Description
sex character Gender
race character Racial group
ethnic_origin character Ethnic origin (hispanic or non-hispanic)
age character Age group
year double Year
quarter double Quarter
n_persons double Number of persons employed by group
median_weekly_earn double Median weekly earning in current dollars

The datasets are collected from the Current Population Survey (CPS) which is a monthly survey of households conducted by the Bureau of Census for the Bureau of Labor Statistics.

Here are some findings when looking through the methods used to tidy and wrangle data from the original source:

Based on the datasets, five questions are going to be explored and analyzed in the following section, including:

3 Analysis and Findings

3.1 What are the changes of people employed in different industries from 2015 to 2020? - Sahinya to edit

Number of people employed across industry from 2015 to 2020

Figure 3.1: Number of people employed across industry from 2015 to 2020

First of all, Figure 3.1 shown above indicates the changes of all the population of employees from different industries in recent 5 years. To be more specific, there is a large number of people working in the industry of education and health services, and the population stayed stably between 34 million and 35 million during 2015 to 2020. However, as the industry of private households hold the least population, the number of people employed in this industry actually decreased from around 0.7 million to 0.6 million. In addition, all industries experienced the decrease of people employed within the industries from 2019 to 2020 except for the public administration.

3.2 What are the demographic differences between industries from 2015 to 2020? - Sahinya to edit

3.2.1 Gender

Figure 3.2: Distribution of men and women across industries

In the analysis about genders in different industries, it is found that there are only five industries that have more female employees than male, which are education and health services, financial activities, leisure and hospitality, other services and private households (Figure 3.2). Especially in the industry of education and services, the number of female employees is more than twice as much as the number of male employees. On the contrary, male workers occupy most of the roles in some industries like manufacturing, construction, transportation and utilities and durable goods. More than 90% of the employees are male in the industry of construction.

3.2.2 Race

Figure 3.3: Distribution of different races across industries

According to Figure 3.3, when looking at the relationships between industries and the population employed among races from the data, most of the people employed among all the industries are white people, following by Black or African American and Asian.

3.3 At what age do men and women work the most and how does the age factor contribute towards employement? - Sahinya

Employment rate by gender and age group

Figure 3.4: Employment rate by gender and age group

It can be observed from Figure 3.4 that both men and women in between the age 16 to 54 have been employed more when compared to other age groups. It is also evident that the number of male employees are more when compared to women. There is a peak in 25-54 age group as this is the age when people finish education and start their career. This also happens to be the prime working time in most of their lives. As one intends the curve to be, there is a peak and the 25-54 age group and the numbers slowly go down after 55 years as people start their retirement phase.

3.4 How do different factors affect the income between 2010 and 2020? - Rachel to edit

When taking a look at the earning data, median weekly income varies through different genders, races and age groups.

3.4.1 Gender & Race

Race and gender do play significant role in income

Figure 3.5: Race and gender do play significant role in income

Figure 3.5 indicates that gender and race do play significant role in affecting weekly income through the past ten years. A clear upward trend in income can be observed in general over the period, the upper vertex of the segments represents male’s income and the lower one represents female’s, which clearly shows that men generally earn more than women in all years and races from 2010 to 2020. In addition, the plot suggests that race is also a key factor affecting income. A surprising finding is that while the number of Asians employed are very low across all industries, Asians actually have the highest median weekly income among the three races recorded, followed by the white race while the black or African American earns the least. This may reflect differences in the amount of time and energy that people of different races are willing to devote to their jobs, Asians are well-known for hard working and are more likely to work extra hours compare with the other two races. On the other hand, another possible reason is that there is a common belief that Asians are smart and tend to be educated for high-income occupations such as doctors and lawyers, while Black and African Americans may suffer from racial discrimination and are forced to work in low-income jobs.

3.4.2 Age group

Median weekly income by year and age group

Figure 3.6: Median weekly income by year and age group

Based on Figure 3.6, income levels at different age groups are all growing over the years. The Y-axis is divided by the minimum, 1/4 quantile, median, 3/4 quantile and maximum income of the total median weekly income. The plot interactively demonstrates that young adults earn much less than middle-aged people and there’s not much difference between age groups over 35.

3.5 How significant is gender and race in affecting earnings?

4 Conclusion - Rachel to edit

Based on the analysis above, it is obvious that gender, race and age group do play significant role in both employment and earning across industries in the US. Industries that generally require more physical labor and technical skills are overwhelmingly dominated by male, such as construction and transport, whereas industries with more women generally require more patience and carefulness, such as education and health. In terms of earnings, men generally earn more than women, which illustrates that a higher level of technology or professional skills may lead to higher income, and gender discrimination could be an issue in higher income groups and occupations.

  1. In terms of race, the number of white people employed is much higher than that of other races. On the one hand, the white population base in the United States is much higher than that of Asians, and on the other hand, whites are not subject to racial discrimination while blacks or African Americans are often the first victims. However, from the income, we find that Asians earn the most. This is mainly due to the fact that Asian immigrants to the United States generally have high education and/or high technology, and generally have high economic strength and educational level, including overseas students. Thus, their income is relatively high. Blacks and Hispanics or Latinos earn less possibly because of employment discrimination or laziness.

  2. In terms of age groups, middle aged people earn most, a lot higher than young adults, which is rather reasonable. This indicates that the United States attaches great importance to work experience and has a relatively stable prospect of promotion and salary increase, which to some extent reflects the sound and stable corporate policy and roughly perfect social welfare system in the United States.

  3. The decline in U.S. employment from 2019 to 2020 is likely due to an increase in layoffs during the economic depression. (Possibly affected by COVID-19)

5 References

Fekedulegn.D, Alterman.T, Charles.L, Kershaw.K, Safford.M, Howard.V, MacDonald.L (2019).Prevalence of workplace discrimination and mistreatment in a national sample of older U.S. workers: The REGARDS cohort study, SSM - Population Health, Volume 8, 100444, ISSN 2352-8273, https://doi.org/10.1016/j.ssmph.2019.100444.

5.1 Data source

Labor Force Statistics from the Current Population Survey. (2021). Retrieved 15 August 2021, from https://www.bls.gov/cps/tables.htm#charemp_m

Tidytuesday. (2021). Retrieved 15 August 2021, from https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-02-23/readme.md

5.2 Software

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

5.3 Packages

Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.5. https://CRAN.R-project.org/package=dplyr

Hadley Wickham (2021). tidyr: Tidy Messy Data. R package version 1.1.3.https://CRAN.R-project.org/package=tidyr

Hao Zhu (2021). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.3.4. https://CRAN.R-project.org/package=kableExtra

JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone (2021). rmarkdown: Dynamic Documents for R. R package version 2.10. URL https://rmarkdown.rstudio.com.

Kirill Müller and Hadley Wickham (2021). tibble: Simple Data Frames. R package version 3.1.3. https://CRAN.R-project.org/package=tibble

Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686

Wilke, C.O. (2020). ggtext: Improved Text Rendering Support for ‘ggplot2’. R package version 0.1.1. https://CRAN.R-project.org/package=ggtext

Yihui Xie and J.J. Allaire and Garrett Grolemund (2018). R Markdown: The Definitive Guide. Chapman and Hall/CRC. ISBN 9781138359338. URL https://bookdown.org/yihui/rmarkdown.

Yihui Xie and Christophe Dervieux and Emily Riederer (2020). R Markdown Cookbook. Chapman and Hall/CRC. ISBN 9780367563837. URL https://bookdown.org/yihui/rmarkdown-cookbook.